Imaging attractor dynamics in the neural compass

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Individual neurons work on short timescales; from an action potential duration of 1 ms to synaptic currents that persist for just 10 ms or 100 ms of milliseconds. Yet, neurons embedded within a network are capable of maintaining persistent activity in the absence of external stimuli. One prominent computational theory uses 'attractor dynamics', whereby the connectivity of the network stabilizes activity without additional input to the network. In general, this is implemented in network that consists of recurrent excitatory connections between neurons that code for similar features along with feedforward inhibitory connectivity to silence the activity of neurons coding for non-similar features. This creates a competitive network that will sustain its current activity without any additional input, and will not change until a new input is strong enough to change the ‘attractor state’. Importantly, this framework can encode discrete and continuous ‘analog’ variables. Here we propose to look for evidence of attractor dynamics in the head direction (HD) cell network.?